A fault monitoring approach using model-based and neural network techniques applied to input–output feedback linearization control induction motor

Harzelli, Imadeddine; Menacer, Arezki; Ameid, Tarek

doi:10.1007/s12652-019-01307-0

Cited by 18 publications

(10 citation statements)

References 38 publications

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“…The study of any physical system often requires a modelling in order to simulate its behaviour against different constraints; to highlights the influence of defects on a measurable magnitude of the machine, and ensures to apprehend the mechanisms governing its operation [45,46]. The development of a mathematical reduced model is obtained from the multi-winding model of IM as shown in Fig.…”

Section: Reduced Model Of Im Taking Into Account the Faultsmentioning

confidence: 99%

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

Harzelli¹,

Benakcha²,

Ameid³

et al. 2021

J. Model. Optim.

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In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM.

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Section: Reduced Model Of Im Taking Into Account the Faultsmentioning

confidence: 99%

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

Harzelli¹,

Benakcha²,

Ameid³

et al. 2021

J. Model. Optim.

Self Cite

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show abstract

“…There are few works describing the applications of artificial intelligence methods in Fault-Tolerant control systems. The works in the literature describe only the use of shallow neural networks, mainly perceptron networks, for IM [23,24]. This approach does not require a priori knowledge of the facility and provides very good results, which is its great advantage.…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of Current Sensor Fault Detection Mechanisms for PMSM Drives Based on Neural Networks

Jankowska

Dybkowski

2022

Designs

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This paper describes a vector-controlled Permanent Magnet Synchronous Motor (PMSM) drive system with the current sensor fault detection mechanism. In general, the control structure is based on the well-known Field Oriented Control (FOC) algorithm. The structure is equipped with an additional algorithm for current sensor fault detection based on a neural network. The presented control structure is able to detect typical current sensor faults, such as lack of signal, intermittent signal, variable gain and signal noise. The application of the NN detector guarantees a faster detection of the sensor fault than classical detectors based on algorithmic methods or logical systems. This work focuses on presenting the methodology of designing detectors and their analysis, based solely on simulation analysis. The simulation results, conducted in the Matlab/Simulink environment, are presented for the above-mentioned faults in phase A and phase B for different speed conditions.

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“…Rotor failures have been studied by different groups of researchers. Classical control has been applied to deal with the presence of degradation problems, fault, and broken rotors in electromechanical systems, such as optimal linear quadratic regulator [9,10], sliding mode control [11], feedback linearization controller [12], and fuzzy logic control [13]. In [9] an optimal linear state feedback control scheme was designed using the optimal linear quadratic regulator technique, for a PMSM with rotor failure.…”

Section: Introductionmentioning

confidence: 99%

“…However, by its nature, the SMC has some inherent problems in its operation such as the chattering phenomenon due to the discontinuity in the switching strategy of the control law. In [12] a feedback linearization controller was made to offer better precision control for induction motor drive subject to short circuit faults in the stator winding and some broken bars in the rotor. An observer based on the feedback of states with the capability to compensate for some failures was designed for two coupling PMSM systems [14].…”

Section: Introductionmentioning

confidence: 99%

Neuroadaptive Robust Speed Control for PMSM Servo Drives with Rotor Failure

et al. 2021

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In this paper, a neuroadaptive robust trajectory tracking controller is utilized to reduce speed ripples of permanent magnet synchronous machine (PMSM) servo drive under the presence of a fracture or fissure in the rotor and external disturbances. The dynamics equations of PMSM servo drive with the presence of a fracture and unknown frictions are described in detail. Due to inherent nonlinearities in PMSM dynamic model, in addition to internal and external disturbances; a traditional PI controller with fixed parameters cannot correctly regulate the PMSM performance under these scenarios. Hence, a neuroadaptive robust controller (NRC) based on a category of on-line trained artificial neural network is used for this purpose to enhance the robustness and adaptive abilities of traditional PI controller. In this paper, the moth-flame optimization algorithm provides the optimal weight parameters of NRC and three PI controllers (off-line) for a PMSM servo drive. The performance of the NRC is evaluated in the presence of a fracture, unknown frictions, and load disturbances, likewise the result outcomes are contrasted with a traditional optimized PID controller and an optimal linear state feedback method.

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A fault monitoring approach using model-based and neural network techniques applied to input–output feedback linearization control induction motor

Cited by 18 publications

References 38 publications

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

Design and Analysis of Current Sensor Fault Detection Mechanisms for PMSM Drives Based on Neural Networks

Neuroadaptive Robust Speed Control for PMSM Servo Drives with Rotor Failure

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